1 Introduction

[Needs more text]

In this document we present a work-flow for integration across different omics datasets.

[Note] This is not the final version of the document.

2 Packages and files

A package with regularized CCA and multiomics DIABLO method is mixOmics. Package igraph is needed for network analysis.

library(mixOmics)
library(igraph)

Package for complex heatmaps.

#BiocManager::install("ComplexHeatmap")
library(ComplexHeatmap)
# https://www.rdocumentation.org/packages/pheatmap/versions/1.0.12/topics/pheatmap
library(pheatmap)

2.1 Functions

Some original and adapted functions can be found in the file that is silently processed here.

%% Additional functions

out <- ""
out <- paste(out,knit_child("005-Functions.Rmd", quiet=TRUE))

2.2 File names

Usually we use a file management system under the pISA-tree framework. For simplicity, all files ( scripts, data ,… ) are in one directory.

Sample description file (aka phenodata)

pfn <- "phenodata_20221001.txt"

Data file names

file1 <- "data_hormonomics.txt"
file2 <- "data_metabolomics.txt"
file3 <- "data_qPCR.txt"

For future use and labeling, we need text names of dataset objects.

datanames <- c("hormonomics",  "metabolomics", "qPCR")

It is advisable to first read the phenodata, followed by actual data input. This enables smart selection of samples, based on the sample selection column with the assay name.

2.3 Phenodata

#(pfn <- getMeta(.ameta
#    , "Phenodata"))
# phenodata file name
pfn
## [1] "phenodata_20221001.txt"
#
phdata <- read.table(pfn
  , header = TRUE
  ,  sep = "\t"
  , stringsAsFactors = FALSE
  , row.names=1
  )
dim(phdata)
## [1] 32 15
names(phdata)
##  [1] "SampleID"                      "Treatment"                    
##  [3] "Harvest"                       "SamplingDay"                  
##  [5] "DaysOfStressH"                 "PlantNo"                      
##  [7] "Sample.type"                   "Date"                         
##  [9] "Heat.Recovery.Days"            "TreatmentxDatexPlant"         
## [11] "TreatmentxSamplingDay"         "TreatmentxSamplingDayxPlantNo"
## [13] "Transcriptomics"               "Metabolomics"                 
## [15] "Hormonomics"
pdata <- phdata

Overview of selected samples:

table(pdata$Treatment, pdata$SamplingDay)
##    
##     1 7 8 14
##   C 4 4 4  4
##   H 4 4 4  4
.treat <- unique(pdata$Treatment)
.days <- unique(pdata$SamplingDay)
.entry <- 0.5
summary(pdata)
##    SampleID          Treatment            Harvest      SamplingDay  
##  Length:32          Length:32          Min.   :1.00   Min.   : 1.0  
##  Class :character   Class :character   1st Qu.:1.75   1st Qu.: 5.5  
##  Mode  :character   Mode  :character   Median :2.50   Median : 7.5  
##                                        Mean   :2.50   Mean   : 7.5  
##                                        3rd Qu.:3.25   3rd Qu.: 9.5  
##                                        Max.   :4.00   Max.   :14.0  
##  DaysOfStressH      PlantNo      Sample.type       
##  Min.   : 0.00   Min.   : 7.00   Length:32         
##  1st Qu.: 0.00   1st Qu.:10.75   Class :character  
##  Median : 0.50   Median :14.50   Mode  :character  
##  Mean   : 3.75   Mean   :14.50                     
##  3rd Qu.: 7.25   3rd Qu.:18.25                     
##  Max.   :14.00   Max.   :22.00                     
##      Date           Heat.Recovery.Days TreatmentxDatexPlant
##  Length:32          Length:32          Length:32           
##  Class :character   Class :character   Class :character    
##  Mode  :character   Mode  :character   Mode  :character    
##                                                            
##                                                            
##                                                            
##  TreatmentxSamplingDay TreatmentxSamplingDayxPlantNo Transcriptomics
##  Length:32             Length:32                     Min.   :1      
##  Class :character      Class :character              1st Qu.:1      
##  Mode  :character      Mode  :character              Median :1      
##                                                      Mean   :1      
##                                                      3rd Qu.:1      
##                                                      Max.   :1      
##   Metabolomics  Hormonomics
##  Min.   :1     Min.   :1   
##  1st Qu.:1     1st Qu.:1   
##  Median :1     Median :1   
##  Mean   :1     Mean   :1   
##  3rd Qu.:1     3rd Qu.:1   
##  Max.   :1     Max.   :1
apply(pdata,2,function(x) summary(as.factor(x)))
## $SampleID
## AD001 AD002 AD003 AD004 AD005 AD006 AD007 AD008 AD013 AD014 AD015 
##     1     1     1     1     1     1     1     1     1     1     1 
## AD016 AD017 AD018 AD019 AD020 AD025 AD026 AD027 AD028 AD037 AD038 
##     1     1     1     1     1     1     1     1     1     1     1 
## AD039 AD040 AD045 AD046 AD047 AD048 AD057 AD058 AD059 AD060 
##     1     1     1     1     1     1     1     1     1     1 
## 
## $Treatment
##  C  H 
## 16 16 
## 
## $Harvest
## 1 2 3 4 
## 8 8 8 8 
## 
## $SamplingDay
##  1  7  8 14 
##  8  8  8  8 
## 
## $DaysOfStressH
##  0  1  7  8 14 
## 16  4  4  4  4 
## 
## $PlantNo
##  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 
##  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2 
## 
## $Sample.type
## adult leaf 
##         32 
## 
## $Date
## 04/11/2020 10/11/2020 11/11/2020 17/11/2020 
##          8          8          8          8 
## 
## $Heat.Recovery.Days
##  0_0  1_0 14_0  7_0  8_0 
##   16    4    4    4    4 
## 
## $TreatmentxDatexPlant
## C_2020-11-04_10  C_2020-11-04_7  C_2020-11-04_8  C_2020-11-04_9 
##               1               1               1               1 
## C_2020-11-10_11 C_2020-11-10_12 C_2020-11-10_13 C_2020-11-10_14 
##               1               1               1               1 
## C_2020-11-11_15 C_2020-11-11_16 C_2020-11-11_17 C_2020-11-11_18 
##               1               1               1               1 
## C_2020-11-17_19 C_2020-11-17_20 C_2020-11-17_21 C_2020-11-17_22 
##               1               1               1               1 
## H_2020-11-04_10  H_2020-11-04_7  H_2020-11-04_8  H_2020-11-04_9 
##               1               1               1               1 
## H_2020-11-10_11 H_2020-11-10_12 H_2020-11-10_13 H_2020-11-10_14 
##               1               1               1               1 
## H_2020-11-11_15 H_2020-11-11_16 H_2020-11-11_17 H_2020-11-11_18 
##               1               1               1               1 
## H_2020-11-17_19 H_2020-11-17_20 H_2020-11-17_21 H_2020-11-17_22 
##               1               1               1               1 
## 
## $TreatmentxSamplingDay
##  C_1 C_14  C_7  C_8  H_1 H_14  H_7  H_8 
##    4    4    4    4    4    4    4    4 
## 
## $TreatmentxSamplingDayxPlantNo
##  C_1_10   C_1_7   C_1_8   C_1_9 C_14_19 C_14_20 C_14_21 C_14_22 
##       1       1       1       1       1       1       1       1 
##  C_7_11  C_7_12  C_7_13  C_7_14  C_8_15  C_8_16  C_8_17  C_8_18 
##       1       1       1       1       1       1       1       1 
##  H_1_10   H_1_7   H_1_8   H_1_9 H_14_19 H_14_20 H_14_21 H_14_22 
##       1       1       1       1       1       1       1       1 
##  H_7_11  H_7_12  H_7_13  H_7_14  H_8_15  H_8_16  H_8_17  H_8_18 
##       1       1       1       1       1       1       1       1 
## 
## $Transcriptomics
##  1 
## 32 
## 
## $Metabolomics
##  1 
## 32 
## 
## $Hormonomics
##  1 
## 32

2.4 Process data files

For this project we aim to integrate several multi-omics datasets. We have data on hormonomics, metabolomics, and qPCR:

hormonomics <- read.table(file1, header=TRUE, sep="\t")
metabolomics <- read.table(file2, header=TRUE, sep="\t")
qPCR <- read.table(file3, header=TRUE, sep="\t")

List dataset names

datanames
## [1] "hormonomics"  "metabolomics" "qPCR"

2.5 Combine datasets

Datasets for DIABLO need to be collected in a list, with rows corresponding to the same samples. The order of samples from shrinked phenodata will be enforced.

The first component of the list will be a grouping variable, indicating the conditions. We will create reasonable names for groups.

sday <- paste0(0,pdata$SamplingDay)
len <- nchar(sday)
sday <- substr(sday,len-1,len)
trt <- pdata$Treatment
what <- paste(trt,sday,sep="")
what
##  [1] "C01" "C01" "C01" "C01" "C07" "C07" "C07" "C07" "C08" "C08" "C08"
## [12] "C08" "C14" "C14" "C14" "C14" "H01" "H01" "H01" "H01" "H07" "H07"
## [23] "H07" "H07" "H08" "H08" "H08" "H08" "H14" "H14" "H14" "H14"
X <- list(status= what)
names(X[[1]]) <- rownames(pdata)
X
## $status
##  C_S1_10   C_S1_7   C_S1_8   C_S1_9  C_S7_11  C_S7_12  C_S7_13 
##    "C01"    "C01"    "C01"    "C01"    "C07"    "C07"    "C07" 
##  C_S7_14  C_S8_15  C_S8_16  C_S8_17  C_S8_18 C_S14_19 C_S14_20 
##    "C07"    "C08"    "C08"    "C08"    "C08"    "C14"    "C14" 
## C_S14_21 C_S14_22  H_S1_10   H_S1_7   H_S1_8   H_S1_9  H_S7_11 
##    "C14"    "C14"    "H01"    "H01"    "H01"    "H01"    "H07" 
##  H_S7_12  H_S7_13  H_S7_14  H_S8_15  H_S8_16  H_S8_17  H_S8_18 
##    "H07"    "H07"    "H07"    "H08"    "H08"    "H08"    "H08" 
## H_S14_19 H_S14_20 H_S14_21 H_S14_22 
##    "H14"    "H14"    "H14"    "H14"
print(addmargins(table(pdata$SamplingDay, what)), zero.print=".")
##      what
##       C01 C07 C08 C14 H01 H07 H08 H14 Sum
##   1     4   .   .   .   4   .   .   .   8
##   7     .   4   .   .   .   4   .   .   8
##   8     .   .   4   .   .   .   4   .   8
##   14    .   .   .   4   .   .   .   4   8
##   Sum   4   4   4   4   4   4   4   4  32
print(addmargins(table(pdata$Treatment, what)), zero.print=".")
##      what
##       C01 C07 C08 C14 H01 H07 H08 H14 Sum
##   C     4   4   4   4   .   .   .   .  16
##   H     .   .   .   .   4   4   4   4  16
##   Sum   4   4   4   4   4   4   4   4  32

Put datasets into the list X and ensure that they all have same order of samples as in phenodata.

datanames
## [1] "hormonomics"  "metabolomics" "qPCR"
i <- 1
for(i in 1:length(datanames)){
x <- get(datanames[i])
rownames(x) <- x[,1]
x <- x[,-1]
X[[i+1]] <- x[rownames(pdata),]
names(X)[i+1] <- datanames[i]
}
str(X)
## List of 4
##  $ status      : Named chr [1:32] "C01" "C01" "C01" "C01" ...
##   ..- attr(*, "names")= chr [1:32] "C_S1_10" "C_S1_7" "C_S1_8" "C_S1_9" ...
##  $ hormonomics :'data.frame':    32 obs. of  12 variables:
##   ..$ IAA       : num [1:32] 37.2 45.9 47.6 37.6 49.5 ...
##   ..$ oxIAA     : num [1:32] 61.5 67.8 52.5 48.7 76.8 ...
##   ..$ IAA.Asp   : num [1:32] 2.22 1.99 1.5 2.24 1.93 ...
##   ..$ ABA       : num [1:32] 36.8 33.1 41.7 39.1 80 ...
##   ..$ PA        : num [1:32] 92 92.6 93.8 91.4 231.7 ...
##   ..$ DPA       : num [1:32] 45.6 62.1 55.1 59.8 178.1 ...
##   ..$ SA        : num [1:32] 505 519 275 628 1315 ...
##   ..$ JA        : num [1:32] 2.69 2.8 4.76 4.62 6.1 ...
##   ..$ JA.Ile    : num [1:32] 0.553 0.566 0.427 0.203 0.623 ...
##   ..$ X9.10.dhJA: num [1:32] 5.45 3.7 2.88 5.17 5.01 ...
##   ..$ X12.OH.JA : num [1:32] 16.1 24 27.9 25.7 244.1 ...
##   ..$ cisOPDA   : num [1:32] 354 403 645 750 1295 ...
##  $ metabolomics:'data.frame':    32 obs. of  22 variables:
##   ..$ Glukose : num [1:32] 2.13 2.2 0.82 2.55 4.77 6.3 7.24 3.09 6.34 9.49 ...
##   ..$ Fructose: num [1:32] 2.7 2.9 1.59 3.01 3.97 7.16 5.41 4.04 8.52 7.75 ...
##   ..$ Sucrose : num [1:32] 3.45 3.38 2.45 4.06 3.53 ...
##   ..$ Starch  : num [1:32] 22.06 12.74 9.98 15.13 16.25 ...
##   ..$ Asp     : num [1:32] 1040 844 887 988 793 ...
##   ..$ Glu     : num [1:32] 2514 1966 2068 2348 2109 ...
##   ..$ Asn     : num [1:32] 178 168 167 172 277 ...
##   ..$ Ser     : num [1:32] 598 498 441 538 368 ...
##   ..$ Gln     : num [1:32] 498 409 400 466 266 ...
##   ..$ Gly     : num [1:32] 137.8 104.6 92.1 117.2 68.7 ...
##   ..$ His     : num [1:32] 20.7 13.3 17.1 16.8 13.7 19.4 20.5 17.8 8.8 18.4 ...
##   ..$ Arg     : num [1:32] 26.9 29.1 29.5 24.9 38.3 63.9 38.6 47.3 54.7 67.2 ...
##   ..$ Thr     : num [1:32] 225 208 216 252 189 ...
##   ..$ Ala     : num [1:32] 702 496 515 653 296 ...
##   ..$ Pro     : num [1:32] 48.6 57.3 53.5 58.6 85.8 ...
##   ..$ Tyr     : num [1:32] 25.3 22.1 24.4 22.3 31.2 40.6 50.9 37.1 20.2 34.6 ...
##   ..$ Val     : num [1:32] 54.1 51.3 52.9 58.4 73.1 ...
##   ..$ Met     : num [1:32] 7.3 6.9 8.6 7.9 4.7 8.6 4.5 6.6 3.8 2.2 ...
##   ..$ Ile     : num [1:32] 48.8 47 54.3 52.9 60.4 80.6 62.7 63 29.9 48.3 ...
##   ..$ Lys     : num [1:32] 26 21.8 26.5 26.7 28.4 33.1 29.2 38.4 20.6 41.8 ...
##   ..$ Leu     : num [1:32] 18.8 15.7 19.5 20.1 27.4 27.1 21.3 26.3 38.5 45.7 ...
##   ..$ Phe     : num [1:32] 119 88 86.8 111 98.3 ...
##  $ qPCR        :'data.frame':    32 obs. of  14 variables:
##   ..$ RbohA  : num [1:32] 1.35 1.5 1.26 1.43 1.4 ...
##   ..$ SnRK2  : num [1:32] 1.5 1.63 1.63 1.53 1.12 ...
##   ..$ ACO2   : num [1:32] 0.15 0.196 0.44 0.177 0.537 ...
##   ..$ HSP70  : num [1:32] 1.013 1.067 0.883 1.042 1.028 ...
##   ..$ PR1b   : num [1:32] 0.324 0.154 0.269 0.256 0.227 ...
##   ..$ RD29B  : num [1:32] 0.017 0.0465 0.017 0.0824 1.7519 ...
##   ..$ X13.LOX: num [1:32] 0.7 0.66 0.766 0.734 1.136 ...
##   ..$ P5CS   : num [1:32] 3.648 3.265 2.152 3.156 0.863 ...
##   ..$ ERF1   : num [1:32] 0.56 0.638 0.586 0.664 1.635 ...
##   ..$ CAT1   : num [1:32] 0.64 0.635 0.687 0.705 0.811 ...
##   ..$ CO     : num [1:32] 2.42 5.56 2.74 2.47 1.71 ...
##   ..$ SWEET  : num [1:32] 0.816 1.874 0.931 0.934 1.495 ...
##   ..$ SP6A   : num [1:32] 0.122 0.239 0.33 0.122 3.386 ...
##   ..$ M0ZJG3 : num [1:32] 1.211 1.376 1.007 0.903 2.358 ...
names(X)
## [1] "status"       "hormonomics"  "metabolomics" "qPCR"

Check if sample names in all datasets match.

OK <- TRUE
for(i in 2:length(X)) {
print(ok <- all(names(X[[1]])==rownames(X[[i]])))
OK <- OK&ok
}
## [1] TRUE
## [1] TRUE
## [1] TRUE

Sample names in datasets match.

Put data into safe object DATA.

DATA <- X

We will also need the names of treatment groups.

groups <- unique(pdata$Treatment)
groups
## [1] "C" "H"

3 Multiomics data integration with DIABLO

CCDATA <- DATA
names(CCDATA)
## [1] "status"       "hormonomics"  "metabolomics" "qPCR"
write("Entering 035-DIABLO !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=!TRUE)
write("commandArgs:", "bla.log", append=TRUE)
write(commandArgs(trailingOnly = TRUE), "bla.log",append=TRUE)
write("End commandArgs", "bla.log", append=TRUE)
out <- ""
out <- paste(out,knit_child("035-DIABLO-2.Rmd", quiet=TRUE))
cat(out)

Child: 035-DIABLO-2.Rmd ## DIABLO hormonomics, metabolomics, qPCR ## DIABLO

DIABLO from mixOmics enables integration of more than two datasets.

3.1 Organize data

Thre datasets are organized as a list of matrices with same samples as rows and variables in columns.

data <- CCDATA[-1]
str(data)
## List of 3
##  $ hormonomics :'data.frame':    32 obs. of  12 variables:
##   ..$ IAA       : num [1:32] 37.2 45.9 47.6 37.6 49.5 ...
##   ..$ oxIAA     : num [1:32] 61.5 67.8 52.5 48.7 76.8 ...
##   ..$ IAA.Asp   : num [1:32] 2.22 1.99 1.5 2.24 1.93 ...
##   ..$ ABA       : num [1:32] 36.8 33.1 41.7 39.1 80 ...
##   ..$ PA        : num [1:32] 92 92.6 93.8 91.4 231.7 ...
##   ..$ DPA       : num [1:32] 45.6 62.1 55.1 59.8 178.1 ...
##   ..$ SA        : num [1:32] 505 519 275 628 1315 ...
##   ..$ JA        : num [1:32] 2.69 2.8 4.76 4.62 6.1 ...
##   ..$ JA.Ile    : num [1:32] 0.553 0.566 0.427 0.203 0.623 ...
##   ..$ X9.10.dhJA: num [1:32] 5.45 3.7 2.88 5.17 5.01 ...
##   ..$ X12.OH.JA : num [1:32] 16.1 24 27.9 25.7 244.1 ...
##   ..$ cisOPDA   : num [1:32] 354 403 645 750 1295 ...
##  $ metabolomics:'data.frame':    32 obs. of  22 variables:
##   ..$ Glukose : num [1:32] 2.13 2.2 0.82 2.55 4.77 6.3 7.24 3.09 6.34 9.49 ...
##   ..$ Fructose: num [1:32] 2.7 2.9 1.59 3.01 3.97 7.16 5.41 4.04 8.52 7.75 ...
##   ..$ Sucrose : num [1:32] 3.45 3.38 2.45 4.06 3.53 ...
##   ..$ Starch  : num [1:32] 22.06 12.74 9.98 15.13 16.25 ...
##   ..$ Asp     : num [1:32] 1040 844 887 988 793 ...
##   ..$ Glu     : num [1:32] 2514 1966 2068 2348 2109 ...
##   ..$ Asn     : num [1:32] 178 168 167 172 277 ...
##   ..$ Ser     : num [1:32] 598 498 441 538 368 ...
##   ..$ Gln     : num [1:32] 498 409 400 466 266 ...
##   ..$ Gly     : num [1:32] 137.8 104.6 92.1 117.2 68.7 ...
##   ..$ His     : num [1:32] 20.7 13.3 17.1 16.8 13.7 19.4 20.5 17.8 8.8 18.4 ...
##   ..$ Arg     : num [1:32] 26.9 29.1 29.5 24.9 38.3 63.9 38.6 47.3 54.7 67.2 ...
##   ..$ Thr     : num [1:32] 225 208 216 252 189 ...
##   ..$ Ala     : num [1:32] 702 496 515 653 296 ...
##   ..$ Pro     : num [1:32] 48.6 57.3 53.5 58.6 85.8 ...
##   ..$ Tyr     : num [1:32] 25.3 22.1 24.4 22.3 31.2 40.6 50.9 37.1 20.2 34.6 ...
##   ..$ Val     : num [1:32] 54.1 51.3 52.9 58.4 73.1 ...
##   ..$ Met     : num [1:32] 7.3 6.9 8.6 7.9 4.7 8.6 4.5 6.6 3.8 2.2 ...
##   ..$ Ile     : num [1:32] 48.8 47 54.3 52.9 60.4 80.6 62.7 63 29.9 48.3 ...
##   ..$ Lys     : num [1:32] 26 21.8 26.5 26.7 28.4 33.1 29.2 38.4 20.6 41.8 ...
##   ..$ Leu     : num [1:32] 18.8 15.7 19.5 20.1 27.4 27.1 21.3 26.3 38.5 45.7 ...
##   ..$ Phe     : num [1:32] 119 88 86.8 111 98.3 ...
##  $ qPCR        :'data.frame':    32 obs. of  14 variables:
##   ..$ RbohA  : num [1:32] 1.35 1.5 1.26 1.43 1.4 ...
##   ..$ SnRK2  : num [1:32] 1.5 1.63 1.63 1.53 1.12 ...
##   ..$ ACO2   : num [1:32] 0.15 0.196 0.44 0.177 0.537 ...
##   ..$ HSP70  : num [1:32] 1.013 1.067 0.883 1.042 1.028 ...
##   ..$ PR1b   : num [1:32] 0.324 0.154 0.269 0.256 0.227 ...
##   ..$ RD29B  : num [1:32] 0.017 0.0465 0.017 0.0824 1.7519 ...
##   ..$ X13.LOX: num [1:32] 0.7 0.66 0.766 0.734 1.136 ...
##   ..$ P5CS   : num [1:32] 3.648 3.265 2.152 3.156 0.863 ...
##   ..$ ERF1   : num [1:32] 0.56 0.638 0.586 0.664 1.635 ...
##   ..$ CAT1   : num [1:32] 0.64 0.635 0.687 0.705 0.811 ...
##   ..$ CO     : num [1:32] 2.42 5.56 2.74 2.47 1.71 ...
##   ..$ SWEET  : num [1:32] 0.816 1.874 0.931 0.934 1.495 ...
##   ..$ SP6A   : num [1:32] 0.122 0.239 0.33 0.122 3.386 ...
##   ..$ M0ZJG3 : num [1:32] 1.211 1.376 1.007 0.903 2.358 ...
length(data)
## [1] 3

In addition, outcome, phenotypic state or in our case treatment can also be determined. We have combination of two treatments and four time points.

state <- factor(CCDATA[[1]])
table(state)
## state
## C01 C07 C08 C14 H01 H07 H08 H14 
##   4   4   4   4   4   4   4   4
str(state)
##  Factor w/ 8 levels "C01","C07","C08",..: 1 1 1 1 2 2 2 2 3 3 ...
##  - attr(*, "names")= chr [1:32] "C_S1_10" "C_S1_7" "C_S1_8" "C_S1_9" ...

3.2 Initial analysis

Note: Additional insights can be found at http://mixomics.org/mixdiablo/diablo-tcga-case-study/.

3.3 Pairwise PLS comparisons

list.keepX = c(25, 25) # select arbitrary values of features to keep
list.keepY = c(25, 25)
par(mfrow=c(2,2))
pairs <- combn(1:length(names(data)),2)
nms <- names(data)
outn <- ""
j <- 1
ncomp <- length(data)
cols <- c('orange1', 'lightgreen', "red")
if(length(data)==3) pick <- 1:3 else pick <- c(4,1:3)
cols <- c('orange1', 'brown1', 'lightgreen',"lightblue")[pick]
pchs <- c(16, 17, 15, 18)[pick]
j <- 4
cutoff <- 0.5
for(j in 1:ncol(pairs) ){
    pair <- pairs[,j]
    pair
    X <- CCDATA[[pair[1]+1]]
    Y <- CCDATA[[pair[2]+1]]
    list.keepX <- rep(min(ncol(X), 25), ncomp)
    list.keepY <- rep(min(ncol(Y), 25), ncomp)
    x <- spls(X, Y, ncomp=ncomp, keepX = list.keepX, keepY = list.keepY)
    assign(paste0("spls",j),x)
    cat("\n",paste(nms[pair]), "\n")
    cat("Results in:",paste0("spls",j),"\n")
    cat("Correlation between pls variates:\n")
    print(round(cor(x$variates$X, x$variates$Y),5))
#
   plotVar(x, cutoff = cutoff, title = paste(nms[pair],collapse=", "),
        legend = c(nms[pair][1], nms[pair][2]),
        var.names = FALSE, style = 'graphics',
        pch = pchs[pair], cex = c(2,2),
        col = cols[pair])
}
## 
##  hormonomics metabolomics 
## Results in: spls1 
## Correlation between pls variates:
##         comp1    comp2   comp3
## comp1 0.91126  0.00000 0.00000
## comp2 0.13029  0.80764 0.00000
## comp3 0.00165 -0.00098 0.61826
## 
##  hormonomics qPCR 
## Results in: spls2 
## Correlation between pls variates:
##          comp1   comp2 comp3
## comp1  0.69330 0.00000 0.000
## comp2 -0.40812 0.63635 0.000
## comp3 -0.31053 0.39759 0.549
## 
##  metabolomics qPCR 
## Results in: spls3 
## Correlation between pls variates:
##         comp1   comp2   comp3
## comp1 0.74472 0.00000 0.00000
## comp2 0.38516 0.60146 0.00000
## comp3 0.27693 0.40129 0.61882
Circle Correlation Plots for pairwise PLS models on ADAPT data. At most top 25 features for each dimension with correlation above 0.5, are displayed.

Circle Correlation Plots for pairwise PLS models on ADAPT data. At most top 25 features for each dimension with correlation above 0.5, are displayed.

3.4 Initial DIABLO model

Following the suggestion in the source, we will use design matrices with small values. This is supposed to keep low classification error rate.

entry <- .entry
design = matrix(entry, ncol = length(data), nrow = length(data),
                dimnames = list(names(data), names(data)))
diag(design) = 0 # set diagonal to 0s

design
##              hormonomics metabolomics qPCR
## hormonomics          0.0          0.5  0.5
## metabolomics         0.5          0.0  0.5
## qPCR                 0.5          0.5  0.0

With a design in place, the initial DIABLO model can be generated. An arbitrarily high number of components (ncomp = 5) will be used.

# form basic DIABLO model
Y <- state
basic.diablo.model = block.splsda(X = data, Y = Y, ncomp = 5, design = design)
## Design matrix has changed to include Y; each block will be
##             linked to Y.

3.5 Tuning the number of components

Details of tuning process can be found in the http://mixomics.org/mixdiablo/diablo-tcga-case-study/.

The process can be computer time consuming and was performed separately.

%% To choose the number of components for the final DIABLO model, the function perf() is run with 3-fold cross-validation repeated 10 times. Fold number should be smaller than minimal number of samples in groups. %% %% %% {r eval=FALSE} %% # Tuning of features can take a substantial amount of time. %% # Chunk not evaluated for this document. %% # %% # run component number tuning with repeated CV %% system.time(perf.diablo = perf(basic.diablo.model, validation = 'Mfold', %% folds = 3, nrepeat = 10)) %% %% plot(perf.diablo) # plot output of tuning %% %% %% %% %% {r eval=FALSE} %% # Tuning of features can take a substantial amount of time. %% # Chunk not evaluated for this document. %% # %% # set the optimal ncomp value %% ncomp <- perf.diablo$choice.ncomp$WeightedVote["Overall.BER", "centroids.dist"] %% # show the optimal choice for ncomp for each dist metric %% perf.diablo$choice.ncomp$WeightedVote %% %% %% For classification, the analysis suggests %% the number of components.

From previous tuning sessions one can conclude, that the classification rate stays roughly unchanged after two to four components, so we will set the number of components to the number of data sets:

ncomp <- length(data)
ncomp
## [1] 3

3.6 Tuning the number of features

We choose the optimal number of variables to select in each data set using the tune.block.splsda() function, for a grid of keepX values for each type of omics. Note that the function has been set to favour a relatively small signature while allowing us to obtain a sufficient number of variables for downstream validation and/or interpretation. See ?tune.block.splsda.

%% The function tune is run with 10-fold cross validation, but repeated only once. Note that for a more thorough tuning process, provided sufficient computational time, we could increase the nrepeat argument. Here we have saved the results into an RData object that is available for download as the tuning can take a very long time, especially on lower end machines. %% %% %% {r } %% x <- list() %% for (i in 1:length(data)){ %% x[[i]] <- c( seq(5,min(30, ncol(data[[i]])) ,5)) %% } %% names(x) <- names(data) %% test.keepX <- x %% test.keepX %% #list (c(5:9, seq(10, 18, 2), seq(20,30,5)), %% # c(5:9, seq(10, 18, 2), seq(20,30,5)), %% # c(5:9, seq(10, 18, 2), seq(20,30,5))) %% %% %% %% %% {r eval=FALSE} %% # Tuning of features can take a substantial amount of time. %% # Chunk not evaluated for this document. %% # %% # run the feature selection tuning %% system.time(tune.model <- tune.block.splsda(X = data, Y = Y, ncomp = ncomp, cpus=4, %% test.keepX = test.keepX, design = design, %% validation = 'Mfold', folds = 3, nrepeat = 1, %% dist = "centroids.dist") %% ) %% %% %% {r eval=FALSE} %% # Tuning of features can take a substantial amount of time. %% # Chunk not evaluated for this document. %% # %% # run the feature selection tuning %% system.time(tune.model <- tune.block.splsda(X = data, Y = Y, ncomp = ncomp, cpus=4, %% test.keepX = test.keepX, design = design, %% validation = 'loo', folds = 3, nrepeat = 1, %% dist = "centroids.dist") %% ) %% %% %% The number of features to select on each component is returned in %% {r eval=FALSE} %% # Tuning of features can take a substantial amount of time. %% # Chunk not evaluated for this document. %% # %% list.keepX = tune.model$choice.keepX # set the optimal values of features to retain %% list.keepX %%

%% Previous analyses suggest the following list: %% %% $metabolomics
%% [1] 10 10 5
%% $hormonomics
%% [1] 5 5 10
%% $qPCR
%% [1] 10 10 5

We have decided to keep 10 variates for each component.

keepX <- list(
    metabolomics = rep(10, ncomp),
    hormonomics = rep(10, ncomp),
    qPCR = rep(10, ncomp)
    )
list.keepX = list()
for (i in 1:length(data)) list.keepX[[i]] <- keepX[[names(data)[i]]]
names(list.keepX) <- names(data)
list.keepX
## $hormonomics
## [1] 10 10 10
## 
## $metabolomics
## [1] 10 10 10
## 
## $qPCR
## [1] 10 10 10

3.7 Final model

The final DIABLO model is run as:

# set the optimised DIABLO model
final.diablo.model = block.splsda(X = data, Y = as.factor(state)
                          ,  ncomp = ncomp
                          , keepX = list.keepX
                          , design = design)
## Design matrix has changed to include Y; each block will be
##             linked to Y.

The selected variables can be extracted with the function selectVar(), for example in each block, as seen below. Note that the stability of selected variables can be extracted from the output of the perf() function.

# the features selected from components
for (comp in 1:ncomp){
cat("\nComponent ", comp,":\n")
for(i in 1:length(data)){
cat(names(data)[i],"\n")
print(selectVar(final.diablo.model, comp = comp)[[i]]$name)
}
}
## 
## Component  1 :
## hormonomics 
##  [1] "DPA"        "SA"         "PA"         "X12.OH.JA"  "X9.10.dhJA"
##  [6] "JA.Ile"     "ABA"        "IAA"        "IAA.Asp"    "cisOPDA"   
## metabolomics 
##  [1] "Glukose"  "Fructose" "Val"      "Ile"      "Tyr"      "Lys"     
##  [7] "His"      "Gln"      "Pro"      "Met"     
## qPCR 
##  [1] "X13.LOX" "PR1b"    "CAT1"    "SP6A"    "M0ZJG3"  "HSP70"  
##  [7] "SWEET"   "RbohA"   "SnRK2"   "ERF1"   
## 
## Component  2 :
## hormonomics 
##  [1] "ABA"       "oxIAA"     "IAA"       "cisOPDA"   "JA.Ile"   
##  [6] "PA"        "IAA.Asp"   "SA"        "DPA"       "X12.OH.JA"
## metabolomics 
##  [1] "Starch"  "Ser"     "Asn"     "His"     "Met"     "Arg"    
##  [7] "Sucrose" "Gly"     "Pro"     "Glukose"
## qPCR 
##  [1] "SP6A"   "SnRK2"  "RD29B"  "CO"     "P5CS"   "PR1b"   "HSP70" 
##  [8] "M0ZJG3" "ACO2"   "RbohA" 
## 
## Component  3 :
## hormonomics 
##  [1] "JA.Ile"     "JA"         "oxIAA"      "IAA.Asp"    "X9.10.dhJA"
##  [6] "DPA"        "PA"         "SA"         "IAA"        "cisOPDA"   
## metabolomics 
##  [1] "Met" "Gln" "Ala" "Leu" "Phe" "Gly" "Glu" "Ile" "Arg" "Asp"
## qPCR 
##  [1] "ACO2"    "PR1b"    "X13.LOX" "RD29B"   "CO"      "M0ZJG3" 
##  [7] "SnRK2"   "P5CS"    "ERF1"    "SWEET"

3.8 Sample plots

plotDIABLO() is a diagnostic plot to check whether the correlation between components from each data set has been maximised as specified in the design matrix. We specify which dimension to be assessed with the ncomp argument.

for(comp in 1:ncomp){
plotDiablo(final.diablo.model, ncomp = comp)
title(paste("Component",comp), adj=0.1, line=-1, outer=TRUE)
}

The sample plot with the plotIndiv() function projects each sample into the space spanned by the components of each block. Clustering of the samples can be assessed with this plot.

plind <- plotIndiv(final.diablo.model, 
          ind.names = FALSE, 
          legend = TRUE,
          title = 'DIABLO Sample Plots',
          guide="none",
          ellipse = TRUE
          )
## Warning: It is deprecated to specify `guide = FALSE` to remove a
## guide. Please use `guide = "none"` instead.

In the arrow plot below, the start of the arrow indicates the centroid between all data sets for a given sample and the tips of the arrows indicate the location of that sample in each block. Such graphics highlight the agreement between all data sets at the sample level. While somewhat difficult to interpret, even qualitatively, this arrow plot shows proximities of C01 and H01 (both day 1), C07 and C08, and H07 and H08 ( both a day apart). While C samples are in forth quadrant ( D1 < 0, D2 > 0), H samples have ( D1 < 0, D2 < 0) except H14 that is separated on the positive part of Dimension 1.

plotArrow(final.diablo.model, ind.names = FALSE, legend = TRUE,
          title = paste(groups,collapse=", ")

          )

3.9 Variable plots

Several graphical outputs are available to visualise and mine the associations between the selected variables.

The best starting point to evaluate the correlation structure between variables is with the correlation circle plot. A majority of the qPCR variables are positively correlated only with the first component. The hormonomics and metabolomics variables seem to separate along first two dimensions. These first two components correlate highly with the selected variables from the all three dataset. From this, the correlation of each selected feature from all three datasets can be evaluated based on their proximity.

#if(length(data)==3) pick <- 1:3 else pick <- c(4,1:3)
#cols <- c('orange1', 'brown1', 'lightgreen',"lightblue")[pick]
#pchs <- c(16, 17, 15, 18)[pick]
cols <- c('orange1', 'brown1', 'lightgreen')
pchs <- c(16, 17, 15)
plotVar(final.diablo.model, var.names = FALSE,
        style = 'graphics', legend = TRUE
        , pch = pchs, cex = rep(2,length(data))
        , col = cols
)

The circos plot is exclusive to integrative frameworks and represents the correlations between variables of different types, represented on the side quadrants. It seems that the qPCR variables are almost entirely negatively correlated with the other two dataframes. Just few from metabolomics and hormonomics are positively correlated. Note that these correlations are above a value of 0.7 (cutoff = 0.7). All interpretations made above are only relevant for features with very strong correlations.

Plot variables

#plotVar(res, cutoff=0.5, legend = TRUE, overlap=!FALSE, style='graphics')
#plotVar(res, cutoff=0.5, legend = TRUE, overlap=FALSE, style='graphics')
plotVar(final.diablo.model, cutoff=0.5, legend = TRUE, comp=c(1,2), overlap=FALSE, style='ggplot2', col=cols)

plotVar(final.diablo.model, cutoff=0.5, legend = TRUE, comp=c(2,3), overlap=FALSE, col=cols)

circosPlot(final.diablo.model, cutoff = 0.7, line = TRUE,
           color.blocks= cols,
           color.cor = c(3,2), size.labels = 1
           , xpd=TRUE)

3.10 Relevance networks

Another visualisation of the correlations between the different types of variables is the relevance network, which is also built on the similarity matrix (as is the circos plot). Colour represent variable type.

blocks <- combn(length(data),2)
j <- 1
cutoff <- 0.8
out35a <- ""
for(j in 1:ncol(blocks)){
    out35a <- paste( out35a, knit_child("035a-DIABLO-network.Rmd", quiet=!TRUE))

        if(interactive()) readline()
}
cat(out35a)

3.10.1 hormonomics and metabolomics

nfn <- paste0("network-035a-",paste(names(data)[blocks[,j]], collapse="-"),"-",cutoff*10)
#nfn <- paste0("network-035a-",j,"-",cutoff*10)
nfn
## [1] "network-035a-hormonomics-metabolomics-8"
write(nfn, "bla.log", append=TRUE)
png(paste0(nfn,".png"), res = 600, width = 4000, height = 4000)
nw <- network(final.diablo.model
        , blocks = blocks[,j]
        , color.node = cols[blocks[,j]]
        , cutoff = cutoff
        , shape.node = "rectangle"
        , save = "png"
       , name.save = nfn
        )
#title(main=paste(names(data)[blocks[,j]], sep=", "),
#sub=paste("Cutoff = ",cutoff))
#        
#dev.off()

Cutoff = 0.8

network-035a-hormonomics-metabolomics-8
network-035a-hormonomics-metabolomics-8

3.10.2 hormonomics and qPCR

nfn <- paste0("network-035a-",paste(names(data)[blocks[,j]], collapse="-"),"-",cutoff*10)
#nfn <- paste0("network-035a-",j,"-",cutoff*10)
nfn
## [1] "network-035a-hormonomics-qPCR-8"
write(nfn, "bla.log", append=TRUE)
png(paste0(nfn,".png"), res = 600, width = 4000, height = 4000)
nw <- network(final.diablo.model
        , blocks = blocks[,j]
        , color.node = cols[blocks[,j]]
        , cutoff = cutoff
        , shape.node = "rectangle"
        , save = "png"
       , name.save = nfn
        )
#title(main=paste(names(data)[blocks[,j]], sep=", "),
#sub=paste("Cutoff = ",cutoff))
#        
#dev.off()

Cutoff = 0.8

network-035a-hormonomics-qPCR-8
network-035a-hormonomics-qPCR-8

3.10.3 metabolomics and qPCR

nfn <- paste0("network-035a-",paste(names(data)[blocks[,j]], collapse="-"),"-",cutoff*10)
#nfn <- paste0("network-035a-",j,"-",cutoff*10)
nfn
## [1] "network-035a-metabolomics-qPCR-8"
write(nfn, "bla.log", append=TRUE)
png(paste0(nfn,".png"), res = 600, width = 4000, height = 4000)
nw <- network(final.diablo.model
        , blocks = blocks[,j]
        , color.node = cols[blocks[,j]]
        , cutoff = cutoff
        , shape.node = "rectangle"
        , save = "png"
       , name.save = nfn
        )
#title(main=paste(names(data)[blocks[,j]], sep=", "),
#sub=paste("Cutoff = ",cutoff))
#        
#dev.off()

Cutoff = 0.8

network-035a-metabolomics-qPCR-8
network-035a-metabolomics-qPCR-8

3.11 Multipartite network

cutoff <- 0.0
x <- final.diablo.model
layout.fun <- NULL
label <- paste(.treat, collapse=", ")
out35b <- ""
  out35b <- paste( out35b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
cat(out35b)

3.11.1 Cutoff = 0

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-C-H-0
network-035b-C-H-0
# Save network layout in ly, used by my.layout function.
if(exists(deparse(substitute(nw)))) ly <- nw$layout else ly <- NULL
cutoff <- 0.8
x <- final.diablo.model
label <- paste(.treat, collapse=", ")
out35b <- ""
  out35b <- paste( out35b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
cat(out35b)

3.11.2 Cutoff = 0.8

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-C-H-8
network-035b-C-H-8

3.12 Visualize loadings

The function plotLoadings() visualises the loading weights of each selected variable on each component and each data set. The colour indicates the class in which the variable has the maximum level of expression (contrib = ‘max’) using the median (method = ‘median’). Figure below depicts the loading values for each dimension.

for(i in 1:ncomp)
plotLoadings(final.diablo.model, comp = i, contrib = 'max', method = 'median')

3.13 Heatmap (clustered image map)

The cimDIABLO() function is a clustered image map specifically implemented to represent the multi-omics molecular signature expression for each sample. From figure below the areas of homogeneous expression levels for a set of samples across a set of features can be determined. For instance, the H14 samples were the only group to show extremely high levels of expression for a specific set of genes and metabolites. This indicates these features are fairly discriminating for this subtype.

cimfn <- "cim.png"
png(cimfn, res = 600, width = 4000, height = 4000)
cimDiablo(final.diablo.model, size.legend=0.7)
dev.off()
## pdf 
##   2
cim.png
cim.png

3.14 AUC and ROC plots

An AUC plot per block can also be obtained using the function auroc(). The interpretation of this output may not be particularly insightful in relation to the performance evaluation of our methods, but can complement the statistical analysis.

par(mfrow=c(2,2))
for(i in 1:length(data))
auc.splsda = auroc(final.diablo.model, roc.block = names(data[i]),
                   roc.comp = 1, print = FALSE)

res12 <- final.diablo.model

3.15 Final analysis

Design matrix determines which datasets (blocks) are connected. We will connect all blocks.

According to recommendation from tuning, design matrix should have small entries that provide better predictions.

entry <- .entry
design <- matrix(entry, length(data), length(data),
   dimnames = list(names(data), names(data)))
diag(design) <- 0
design
##              hormonomics metabolomics qPCR
## hormonomics          0.0          0.5  0.5
## metabolomics         0.5          0.0  0.5
## qPCR                 0.5          0.5  0.0

Based on tuning part, we can keep two to four components.

list.keepX
## $hormonomics
## [1] 10 10 10
## 
## $metabolomics
## [1] 10 10 10
## 
## $qPCR
## [1] 10 10 10
ncomp = length(list.keepX[[1]])
ncomp
## [1] 3

You can also control the number of variables to be kept for each component.

#keepX <- list(XX=rep(3,ncomp), YY=rep(3,ncomp), ZZ=rep(3, ncomp))
#keepX

We will not restrict variables.

Now we can do the calculation

res <- block.splsda(X = data
    , Y = as.factor(state)
    , ncomp = ncomp
    , keepX = list.keepX
    , design = design
    )
## Design matrix has changed to include Y; each block will be
##             linked to Y.

Model res and final.diablo.model do not differ:

sapply(names(res$loadings),
function(name) all(res$loadings[[name]] -
final.diablo.model$loadings[[name]]==0))
##  hormonomics metabolomics         qPCR            Y 
##         TRUE         TRUE         TRUE         TRUE

Estimate classification error rate. The error rate should drop by more components used.

# run component number tuning with repeated CV
system.time(perf.diablo  <-  perf(res, validation = 'Mfold',
                   folds = 3, nrepeat = 10))

plot(perf.diablo) # plot output of tuning

Names of kept variables

# the features selected to form components
for (comp in 1:ncomp){
cat("\nComponent ", comp,":\n")
for(i in 1:length(data)){
cat(names(data)[i],"\n")
print(selectVar(res, comp = comp)[[i]]$name)
}
}
## 
## Component  1 :
## hormonomics 
##  [1] "DPA"        "SA"         "PA"         "X12.OH.JA"  "X9.10.dhJA"
##  [6] "JA.Ile"     "ABA"        "IAA"        "IAA.Asp"    "cisOPDA"   
## metabolomics 
##  [1] "Glukose"  "Fructose" "Val"      "Ile"      "Tyr"      "Lys"     
##  [7] "His"      "Gln"      "Pro"      "Met"     
## qPCR 
##  [1] "X13.LOX" "PR1b"    "CAT1"    "SP6A"    "M0ZJG3"  "HSP70"  
##  [7] "SWEET"   "RbohA"   "SnRK2"   "ERF1"   
## 
## Component  2 :
## hormonomics 
##  [1] "ABA"       "oxIAA"     "IAA"       "cisOPDA"   "JA.Ile"   
##  [6] "PA"        "IAA.Asp"   "SA"        "DPA"       "X12.OH.JA"
## metabolomics 
##  [1] "Starch"  "Ser"     "Asn"     "His"     "Met"     "Arg"    
##  [7] "Sucrose" "Gly"     "Pro"     "Glukose"
## qPCR 
##  [1] "SP6A"   "SnRK2"  "RD29B"  "CO"     "P5CS"   "PR1b"   "HSP70" 
##  [8] "M0ZJG3" "ACO2"   "RbohA" 
## 
## Component  3 :
## hormonomics 
##  [1] "JA.Ile"     "JA"         "oxIAA"      "IAA.Asp"    "X9.10.dhJA"
##  [6] "DPA"        "PA"         "SA"         "IAA"        "cisOPDA"   
## metabolomics 
##  [1] "Met" "Gln" "Ala" "Leu" "Phe" "Gly" "Glu" "Ile" "Arg" "Asp"
## qPCR 
##  [1] "ACO2"    "PR1b"    "X13.LOX" "RD29B"   "CO"      "M0ZJG3" 
##  [7] "SnRK2"   "P5CS"    "ERF1"    "SWEET"

One would like to reduce the number of nodes, especially for proteomics data. One option is to reduce datasets in a way to keep only the variables in the selectVars in original data in . We will keep variables from the first two components.

keptVars <- unique(c(
 selectVar(res, comp=1)[[1]]$name
,selectVar(res, comp=2)[[1]]$name
)
)
which(keptVars%in%selectVar(res, comp=1)[[1]]$name)
##  [1]  1  2  3  4  5  6  7  8  9 10
which(keptVars%in%selectVar(res, comp=2)[[1]]$name)
##  [1]  1  2  3  4  6  7  8  9 10 11

3.15.1 Visualise variables

Loadings

sapply(res$loadings, head, 30)
## $hormonomics
##                   comp1       comp2       comp3
## IAA         0.036703482 -0.40678302  0.02662456
## oxIAA       0.000000000  0.41472146 -0.46097105
## IAA.Asp    -0.007071215 -0.19836112 -0.38748591
## ABA         0.216332456  0.49851046  0.00000000
## PA          0.426177444  0.25246990  0.07019303
## DPA         0.549700457 -0.08274318  0.14734983
## SA          0.428211919 -0.17502093  0.03899104
## JA          0.000000000  0.00000000  0.48879193
## JA.Ile      0.246436078 -0.32453847 -0.55019009
## X9.10.dhJA  0.297909166  0.00000000 -0.25753363
## X12.OH.JA   0.367626768  0.05459029  0.00000000
## cisOPDA     0.003133045  0.40638498 -0.02327900
## 
## $metabolomics
##                comp1         comp2       comp3
## Glukose   0.48451843  0.0007744579  0.00000000
## Fructose  0.44913578  0.0000000000  0.00000000
## Sucrose   0.00000000  0.1755425740  0.00000000
## Starch    0.00000000  0.6230690186  0.00000000
## Asp       0.00000000  0.0000000000  0.02433324
## Glu       0.00000000  0.0000000000 -0.08664505
## Asn       0.00000000 -0.3852103155  0.00000000
## Ser       0.00000000  0.4187171275  0.00000000
## Gln      -0.13859433  0.0000000000  0.52431197
## Gly       0.00000000  0.0875580979  0.12358545
## His       0.21679051 -0.3308628088  0.00000000
## Arg       0.00000000 -0.2436655212 -0.02652000
## Thr       0.00000000  0.0000000000  0.00000000
## Ala       0.00000000  0.0000000000  0.41741363
## Pro       0.11913529  0.0723114386  0.00000000
## Tyr       0.34922488  0.0000000000  0.00000000
## Val       0.37933829  0.0000000000  0.00000000
## Met      -0.07239333 -0.2748085404  0.62202620
## Ile       0.35208370  0.0000000000  0.08509517
## Lys       0.29674894  0.0000000000  0.00000000
## Leu       0.00000000  0.0000000000 -0.27616600
## Phe       0.00000000  0.0000000000  0.23740522
## 
## $qPCR
##              comp1       comp2        comp3
## RbohA   0.16466558 -0.01682425  0.000000000
## SnRK2   0.13112994 -0.30963253 -0.139934444
## ACO2    0.00000000 -0.04461440 -0.883294093
## HSP70   0.24795487 -0.11801339  0.000000000
## PR1b    0.37477829  0.12308322 -0.306943422
## RD29B   0.00000000  0.28522220  0.155469415
## X13.LOX 0.58349667  0.00000000  0.168233520
## P5CS    0.00000000 -0.14974981  0.066857223
## ERF1    0.09174938  0.00000000 -0.065022964
## CAT1    0.37449858  0.00000000  0.000000000
## CO      0.00000000 -0.15196379  0.150498594
## SWEET   0.21148675  0.00000000  0.004392953
## SP6A    0.34570655  0.85834408  0.000000000
## M0ZJG3  0.31681955 -0.09567284  0.148846821
## 
## $Y
##            comp1       comp2       comp3
## C01 -0.346770705  0.03865545  0.25532621
## C07 -0.110583348  0.11426169  0.19174216
## C08 -0.008904022  0.38818375  0.02515787
## C14  0.063004541  0.64332045 -0.38125662
## H01 -0.287945254 -0.14936480  0.29508918
## H07 -0.116690109 -0.40997461  0.17110929
## H08 -0.065417492 -0.44511069 -0.76872677
## H14  0.873306389 -0.17997122  0.21155868
#plotLoadings(res, comp = 1, method = 'median')
#plotLoadings(res, comp = 1, method = 'median', contrib="max")
for( i in 1:ncomp)
plotLoadings(res, comp = i, method = 'median', contrib="max")

Plot variables

#plotVar(res, cutoff=0.5, legend = TRUE, overlap=!FALSE, style='graphics')
#plotVar(res, cutoff=0.5, legend = TRUE, overlap=FALSE, style='graphics')
plotVar(res, cutoff=0.5, legend = TRUE, comp=c(1,2), overlap=FALSE, style='ggplot2', col=cols)

plotVar(res, cutoff=0.5, legend = TRUE, comp=c(2,3), overlap=FALSE, col=cols)

3.16 Differential networks

Here we will show differential networks between treatments.

cutoffs <- c(0.7)
pairs <- combn(1:length(names(res$X)),2)
outn <- ""
j <- 4
cutoff <- 0.5
for(j in 1:ncol(pairs) ){
    pair <- pairs[,j]
    X <- data[[pair[1]]]
    Y <- data[[pair[2]]]
    datasets <- names(data)[pair]
    outn <- paste( outn, knit_child("023-prepare-networkdiff.Rmd", quiet=TRUE))
   for(cutoff in cutoffs){
   outn <- paste( outn, knit_child("035-Network.Rmd", quiet=TRUE))
   }
}
cat(outn)
size.variables <- 1
sim <-circosPlot(final.diablo.model, cutoff = 0.5, line = TRUE,
           color.blocks= cols,
           color.cor = c(3,2), size.labels = 1
           , size.variables = size.variables
           , xpd=TRUE)

circosPlot(final.diablo.model, cutoff = 0.78, line = TRUE,
           color.blocks= cols,
           color.cor = c(3,2), size.labels = 1
           , size.variables = size.variables
           , xpd=TRUE)

circosPlot(final.diablo.model, cutoff = 0.9, line = TRUE,
           color.blocks= cols,
           color.cor = c(3,2), size.labels = 1
           , size.variables = size.variables
           , xpd=TRUE)

circosPlot(final.diablo.model, cutoff = 0.95, line = TRUE,
           color.blocks= cols,
           color.cor = c(3,2), size.labels = 1
           , size.variables = size.variables
           , xpd=TRUE)

4 Export networks to file

Partial models for each treatment

4.1 Network for C.

filter <- pdata$Treatment %in% .treat[1]
XX1 <- lapply(CCDATA, function(x) if(is.null(dim(x))) x[filter] else x[filter,])
table(XX1$status)
## 
## C01 C07 C08 C14 
##   4   4   4   4
res1 <- block.splsda(X = XX1[-1]
    , Y = as.factor(XX1[[1]])
    , ncomp = ncomp
    , keepX = list.keepX
    , design = design
    )
## Design matrix has changed to include Y; each block will be
##             linked to Y.
cutoff <- 0.0
x <- res1
layout.fun <- NULL
label <-.treat[1]
out23b <- ""
  out23b <- paste( out23b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
N1 <- nw
cat(out23b)

4.1.1 Cutoff = 0

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-C-0
network-035b-C-0

Save network layout for further plots, used by layout function my.layout.

ly <- nw$layout
cutoff <- 0.7
x <- res1
layout.fun <- my.layout
label <- .treat[1]
out23b <- ""
  out23b <- paste( out23b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
cat(out23b)

4.1.2 Cutoff = 0.7

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-C-7
network-035b-C-7

4.2 Network for H.

filter <- pdata$Treatment %in% .treat[2]
XX2 <- lapply(CCDATA, function(x) if(is.null(dim(x))) x[filter] else x[filter,])
table(XX2$status)
## 
## H01 H07 H08 H14 
##   4   4   4   4
res2 <- block.splsda(X = XX2[-1]
    , Y = as.factor(XX2[[1]])
    , ncomp = ncomp
    , keepX = list.keepX
    , design = design
    )
## Design matrix has changed to include Y; each block will be
##             linked to Y.
cutoff <- 0.0
x <- res2
layout.fun <- NULL
label <- .treat[2]
out23b <- ""
  out23b <- paste( out23b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
N2 <- nw
cat(out23b)

4.2.1 Cutoff = 0

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-H-0
network-035b-H-0

Save layout for further plots, used by layout function my.layout.

ly <- nw$layout
cutoff <- 0.7
x <- res2
layout.fun <- my.layout
label <- .treat[2]
out23b <- ""
  out23b <- paste( out23b, knit_child("035b-multipartite-network.Rmd", quiet=TRUE))
cat(out23b)

4.2.2 Cutoff = 0.7

ndata <- length(data)
lbl <- gsub(", ","-",label)
nfn <- paste("network-035b",lbl,cutoff*10,sep="-")
#png(nfn, res = 600, width = 4000, height = 4000)
write(nfn, "bla.log", append=TRUE)
set.seed(1234)
nw <- my.network(x
        , blocks = 1:ndata
        , color.node = cols
        , cutoff = cutoff
        , shape.node = "rectangle"
        , layout = layout.fun
        , save = "png"
        , name.save = nfn
        )
#        title( #main=paste(names(data), sep=", "),
#        sub=paste("Cutoff = ",cutoff))
#        title(label,adj=0.8,outer=TRUE,line=-1)
#        legend("bottomright", pch=15,pt.cex=2,col=cols, legend=names(data),
#        bty="n")
#        text(ly[,1],ly[,2],names(V(nw$gR)))
#dev.off()
network-035b-H-7
network-035b-H-7

Save network file for combined and single treatments. Networks are in objects res, res1 and res2.

write("Mid diablo 5 41 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=TRUE)

4.3 Network for C and H.

# Complete network, cutoff = 0, both
datasets <- names(CCDATA[-1])
ndatasets<- length(datasets)
#
N12 <- network(res
    , cutoff = 0
    , blocks = 1:ndatasets
    , shape.node = c("rectangle")
    , save = "png"
    , name.save="network-CH"
    )
#
e <- extractEdges2(N12)
colnames(e)[ncol(e)] <- paste(.treat, collapse=".")
head(e)
##                                        edge      group1    from
## ho.IAA_me.Glukose         ho.IAA_me.Glukose hormonomics     IAA
## ho.oxIAA_me.Glukose     ho.oxIAA_me.Glukose hormonomics   oxIAA
## ho.IAA.Asp_me.Glukose ho.IAA.Asp_me.Glukose hormonomics IAA.Asp
## ho.ABA_me.Glukose         ho.ABA_me.Glukose hormonomics     ABA
## ho.PA_me.Glukose           ho.PA_me.Glukose hormonomics      PA
## ho.DPA_me.Glukose         ho.DPA_me.Glukose hormonomics     DPA
##                             group2      to         C.H
## ho.IAA_me.Glukose     metabolomics Glukose -0.08555827
## ho.oxIAA_me.Glukose   metabolomics Glukose  0.51707118
## ho.IAA.Asp_me.Glukose metabolomics Glukose -0.22176715
## ho.ABA_me.Glukose     metabolomics Glukose  0.80887195
## ho.PA_me.Glukose      metabolomics Glukose  0.86161713
## ho.DPA_me.Glukose     metabolomics Glukose  0.73483118
tail(e)
##                              edge       group1 from group2     to
## me.Val_qP.M0ZJG3 me.Val_qP.M0ZJG3 metabolomics  Val   qPCR M0ZJG3
## me.Met_qP.M0ZJG3 me.Met_qP.M0ZJG3 metabolomics  Met   qPCR M0ZJG3
## me.Ile_qP.M0ZJG3 me.Ile_qP.M0ZJG3 metabolomics  Ile   qPCR M0ZJG3
## me.Lys_qP.M0ZJG3 me.Lys_qP.M0ZJG3 metabolomics  Lys   qPCR M0ZJG3
## me.Leu_qP.M0ZJG3 me.Leu_qP.M0ZJG3 metabolomics  Leu   qPCR M0ZJG3
## me.Phe_qP.M0ZJG3 me.Phe_qP.M0ZJG3 metabolomics  Phe   qPCR M0ZJG3
##                         C.H
## me.Val_qP.M0ZJG3  0.9022587
## me.Met_qP.M0ZJG3 -0.3590730
## me.Ile_qP.M0ZJG3  0.8768180
## me.Lys_qP.M0ZJG3  0.8442707
## me.Leu_qP.M0ZJG3  0.5974332
## me.Phe_qP.M0ZJG3  0.4424355
dim(e)
## [1] 714   6
# treatment 1
e1 <- extractEdges2(N1)
colnames(e1)[ncol(e1)] <- .treat[1]
head(e1)
##                                        edge      group1    from
## ho.IAA_me.Glukose         ho.IAA_me.Glukose hormonomics     IAA
## ho.oxIAA_me.Glukose     ho.oxIAA_me.Glukose hormonomics   oxIAA
## ho.IAA.Asp_me.Glukose ho.IAA.Asp_me.Glukose hormonomics IAA.Asp
## ho.ABA_me.Glukose         ho.ABA_me.Glukose hormonomics     ABA
## ho.PA_me.Glukose           ho.PA_me.Glukose hormonomics      PA
## ho.DPA_me.Glukose         ho.DPA_me.Glukose hormonomics     DPA
##                             group2      to            C
## ho.IAA_me.Glukose     metabolomics Glukose -0.009706936
## ho.oxIAA_me.Glukose   metabolomics Glukose  0.407917620
## ho.IAA.Asp_me.Glukose metabolomics Glukose -0.268229896
## ho.ABA_me.Glukose     metabolomics Glukose  0.740096225
## ho.PA_me.Glukose      metabolomics Glukose  0.896262542
## ho.DPA_me.Glukose     metabolomics Glukose  0.829405769
dim(e1)
## [1] 662   6
e <- merge(e,e1, sort=FALSE, all=TRUE)
head(e)
##                    edge      group1    from       group2      to
## 1     ho.IAA_me.Glukose hormonomics     IAA metabolomics Glukose
## 2   ho.oxIAA_me.Glukose hormonomics   oxIAA metabolomics Glukose
## 3 ho.IAA.Asp_me.Glukose hormonomics IAA.Asp metabolomics Glukose
## 4     ho.ABA_me.Glukose hormonomics     ABA metabolomics Glukose
## 5      ho.PA_me.Glukose hormonomics      PA metabolomics Glukose
## 6     ho.DPA_me.Glukose hormonomics     DPA metabolomics Glukose
##           C.H            C
## 1 -0.08555827 -0.009706936
## 2  0.51707118  0.407917620
## 3 -0.22176715 -0.268229896
## 4  0.80887195  0.740096225
## 5  0.86161713  0.896262542
## 6  0.73483118  0.829405769
tail(e)
##                edge       group1  from       group2    to       C.H
## 709 me.His_qP.HSP70 metabolomics   His         qPCR HSP70 0.8217718
## 710  me.Lys_qP.ACO2 metabolomics   Lys         qPCR  ACO2 0.6368664
## 711 ho.oxIAA_me.Lys  hormonomics oxIAA metabolomics   Lys 0.2842323
## 712 me.Lys_qP.RbohA metabolomics   Lys         qPCR RbohA 0.5059654
## 713 me.Lys_qP.SnRK2 metabolomics   Lys         qPCR SnRK2 0.8284052
## 714  me.Lys_qP.P5CS metabolomics   Lys         qPCR  P5CS 0.8220844
##      C
## 709 NA
## 710 NA
## 711 NA
## 712 NA
## 713 NA
## 714 NA
# treatment 2
.treat[2]
## [1] "H"
e2 <- extractEdges2(N2)
colnames(e2)[ncol(e2)] <- .treat[2]
head(e2)
##                                        edge      group1    from
## ho.IAA_me.Glukose         ho.IAA_me.Glukose hormonomics     IAA
## ho.oxIAA_me.Glukose     ho.oxIAA_me.Glukose hormonomics   oxIAA
## ho.IAA.Asp_me.Glukose ho.IAA.Asp_me.Glukose hormonomics IAA.Asp
## ho.ABA_me.Glukose         ho.ABA_me.Glukose hormonomics     ABA
## ho.PA_me.Glukose           ho.PA_me.Glukose hormonomics      PA
## ho.DPA_me.Glukose         ho.DPA_me.Glukose hormonomics     DPA
##                             group2      to           H
## ho.IAA_me.Glukose     metabolomics Glukose -0.03135486
## ho.oxIAA_me.Glukose   metabolomics Glukose  0.34316156
## ho.IAA.Asp_me.Glukose metabolomics Glukose -0.24733014
## ho.ABA_me.Glukose     metabolomics Glukose  0.87529961
## ho.PA_me.Glukose      metabolomics Glukose  0.88467893
## ho.DPA_me.Glukose     metabolomics Glukose  0.89945125
dim(e2)
## [1] 688   6
e <- merge(e,e2, sort=FALSE, all=TRUE)
head(e)
##                    edge      group1    from       group2      to
## 1     ho.IAA_me.Glukose hormonomics     IAA metabolomics Glukose
## 2   ho.oxIAA_me.Glukose hormonomics   oxIAA metabolomics Glukose
## 3 ho.IAA.Asp_me.Glukose hormonomics IAA.Asp metabolomics Glukose
## 4     ho.ABA_me.Glukose hormonomics     ABA metabolomics Glukose
## 5      ho.PA_me.Glukose hormonomics      PA metabolomics Glukose
## 6     ho.DPA_me.Glukose hormonomics     DPA metabolomics Glukose
##           C.H            C           H
## 1 -0.08555827 -0.009706936 -0.03135486
## 2  0.51707118  0.407917620  0.34316156
## 3 -0.22176715 -0.268229896 -0.24733014
## 4  0.80887195  0.740096225  0.87529961
## 5  0.86161713  0.896262542  0.88467893
## 6  0.73483118  0.829405769  0.89945125
tail(e)
##                 edge       group1 from group2     to C.H  C         H
## 735  me.Thr_qP.HSP70 metabolomics  Thr   qPCR  HSP70  NA NA 0.2569343
## 736   me.Thr_qP.CAT1 metabolomics  Thr   qPCR   CAT1  NA NA 0.3463942
## 737   me.Thr_qP.ERF1 metabolomics  Thr   qPCR   ERF1  NA NA 0.1878546
## 738   me.Thr_qP.P5CS metabolomics  Thr   qPCR   P5CS  NA NA 0.2701877
## 739     me.Thr_qP.CO metabolomics  Thr   qPCR     CO  NA NA 0.3305915
## 740 me.Thr_qP.M0ZJG3 metabolomics  Thr   qPCR M0ZJG3  NA NA 0.3471071
#
write("Mid diablo 5 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=TRUE)

Compose file name and necessary information for network export file

file <- paste0("network-",paste(.treat, collapse="_"),"-",paste(datasets, collapse="_"),".txt")
label0 <- paste(paste(.treat, collapse=", "),"|",paste(datasets, collapse=", "),"; cutoff =",0)
title <- label0
sets <- 1:length(DATA)
suffix <- paste0(substr(names(DATA),1,2)[sets[-1]],collapse="-")
write("Mid diablo 6 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=TRUE)
write(file.path(suffix,file), "bla.log", append=TRUE)
write(file, "bla.log", append=TRUE)
length(str(e))
## 'data.frame':    740 obs. of  8 variables:
##  $ edge  : chr  "ho.IAA_me.Glukose" "ho.oxIAA_me.Glukose" "ho.IAA.Asp_me.Glukose" "ho.ABA_me.Glukose" ...
##  $ group1: chr  "hormonomics" "hormonomics" "hormonomics" "hormonomics" ...
##  $ from  : chr  "IAA" "oxIAA" "IAA.Asp" "ABA" ...
##  $ group2: chr  "metabolomics" "metabolomics" "metabolomics" "metabolomics" ...
##  $ to    : chr  "Glukose" "Glukose" "Glukose" "Glukose" ...
##  $ C.H   : num  -0.0856 0.5171 -0.2218 0.8089 0.8616 ...
##  $ C     : num  -0.00971 0.40792 -0.26823 0.7401 0.89626 ...
##  $ H     : num  -0.0314 0.3432 -0.2473 0.8753 0.8847 ...
## [1] 0

Export edges table

#e <- data.frame(x=1:10,y=1:10)
#my.write.table(e, file="network.txt",meta=FALSE)
write.table(e, file = file, na="0")

Table with edges for networks based on combined treatments (C, H) and single treatments (C) and (H) is exported as a text file. This table can be used for inspection and filtering out edges based on selected cutoff. Missing edges are labeled as weight 0. This enables numeric filtration in Excel.

write("End diablo 7 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=TRUE)
write("From 035-DIABLO !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!", "bla.log", append=TRUE)

5 SessionInfo

Windows 10 x64 (build 19045)
R version 4.0.2 (2020-06-22) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale: [1] LC_COLLATE=Slovenian_Slovenia.1250 [2] LC_CTYPE=Slovenian_Slovenia.1250
[3] LC_MONETARY=Slovenian_Slovenia.1250 [4] LC_NUMERIC=C
[5] LC_TIME=Slovenian_Slovenia.1250
system code page: 1252

attached base packages: [1] grid stats graphics utils datasets grDevices [7] methods base

other attached packages: [1] pheatmap_1.0.12 ComplexHeatmap_2.6.2 igraph_1.2.6
[4] mixOmics_6.14.0 ggplot2_3.3.5 lattice_0.22-5
[7] MASS_7.3-60.0.1 knitr_1.43

loaded via a namespace (and not attached): [1] ggrepel_0.9.0 Rcpp_1.0.7 circlize_0.4.15
[4] tidyr_1.1.2 corpcor_1.6.9 png_0.1-7
[7] digest_0.6.35 RSpectra_0.16-0 R6_2.5.1
[10] plyr_1.8.6 stats4_4.0.2 ellipse_0.4.2
[13] evaluate_0.21 highr_0.8 pillar_1.4.7
[16] GlobalOptions_0.1.2 rlang_1.1.1 rstudioapi_0.13
[19] jquerylib_0.1.4 S4Vectors_0.28.1 GetoptLong_1.0.5
[22] Matrix_1.6-5 rmarkdown_2.21 labeling_0.4.2
[25] rARPACK_0.11-0 stringr_1.4.0 munsell_0.5.0
[28] compiler_4.0.2 xfun_0.39 pkgconfig_2.0.3
[31] BiocGenerics_0.36.0 shape_1.4.6 htmltools_0.5.2
[34] tidyselect_1.1.0 tibble_3.0.4 gridExtra_2.3
[37] IRanges_2.24.1 matrixStats_1.2.0 crayon_1.3.4
[40] dplyr_1.0.2 withr_3.0.0 jsonlite_1.8.8
[43] gtable_0.3.0 lifecycle_0.2.0 magrittr_2.0.1
[46] scales_1.1.1 cli_3.2.0 stringi_1.5.3
[49] farver_2.0.3 reshape2_1.4.4 bslib_0.3.1
[52] ellipsis_0.3.1 generics_0.1.0 vctrs_0.4.1
[55] rjson_0.2.20 RColorBrewer_1.1-2 tools_4.0.2
[58] Cairo_1.5-15 glue_1.4.2 purrr_0.3.4
[61] parallel_4.0.2 fastmap_1.1.1 yaml_2.2.1
[64] clue_0.3-60 colorspace_1.4-1 cluster_2.1.0
[67] sass_0.4.0